The learnware paradigm focuses on leveraging numerous established high-performing models to solve machine learning tasks instead of starting from scratch. As the key concept of this paradigm, a learnware consists of a well-trained model of any structure and a specification that characterizes the model's capabilities, allowing it to be identified and reused for future tasks. Given the existence of numerous real-world models trained on diverse label spaces, effectively identifying and combining these models to address tasks involving previously unseen label spaces represents a critical challenge in this paradigm. In this paper, we make the first attempt to identify and reuse effective learnware combinations for tackling learning tasks across different label spaces, extending their applicability beyond the original purposes of individual learnwares. To this end, we introduce a statistical class-wise specification for establishing similarity relations between various label spaces. Leveraging these relations, we model the utility of a learnware combination as a minimum-cost maximum-flow problem, and further develop fine-grained learnware identification and assembly methods. Extensive experiments with thousands of heterogeneous models validate our approach, demonstrating that reusing identified learnware combinations can outperform both training from scratch and fine-tuning a generic pre-trained model.

The learnware paradigm proposed by Zhou [34] aims to solve machine learning tasks by leveraging numerous existing high-performing models instead of training from scratch. These models are accommodated in a learnware dock system, where each learnware consists of a model and a specification that characterizes the model's utility, enabling it to be identified for future tasks. A critical challenge in this paradigm remains unresolved: determining what models can be or should be admitted to the system. Without well-established admission criteria, the uncontrolled growth of uploaded models could lead to significant redundancy and inefficiency, resulting in higher storage overhead, increased computational costs, and even potential system failure. To address this gap, this paper presents the first attempt to establish learnware admission criteria and dynamically filter redundant learnwares based on model capability coverage. Specifically, we organize task information from all learnwares into a tree-based structure to assess model capabilities across a continuously expanding task set. Using this structure and model capability representation, we develop an efficient and scalable method for detecting redundant learnwares dynamically without traversing the entire system. Theoretical analysis and extensive experiments involving over ten thousand simulated learnwares validate the efficacy and efficiency of our approach.

The learnware paradigm aims to establish a market of numerous well-performed machine learning models, enabling users to leverage existing helpful models for their tasks instead of starting from scratch. Each learnware in the market is a model submitted by its developer, associated with a specification generated with the help of learnware market, representing the model's specialty and utility and enabling it to be identified for new user tasks. As the market continuously scales up, accommodating an ever-increasing number of learnwares, the critical challenge of the learnware paradigm is to effectively and efficiently identify the most helpful learnware(s) for a new user task without accessing the user's raw data. In this paper, to achieve increasingly accurate learnware characterization and identification along with a growing number of learnwares in the market, we propose an approach called Evolvable Learnware Specification with Index (ELSI). Specifically, based on the key idea of leveraging the task information within learnware specifications, we tackle the challenge of ascertaining the capabilities of models beyond their original training tasks, thereby enabling learnware specifications and the entire market to evolve continuously. Furthermore, through organizing learnwares and constructing specification indexes, we design a practical procedure to accurately and efficiently identify helpful learnwares without examining the entire market. Theoretical analysis and extensive experiments on a learnware market prototype encompassing thousands of models and covering six real-world scenarios validate the effectiveness and efficiency of our approach.

The learnware paradigm proposed by Zhou [40] aims to enable users to leverage numerous existing high-performing models instead of building machine learning models from scratch.This paradigm envisions that: Any developer worldwide can submit their well-trained models spontaneously into a learnware dock system (formerly known as learnware market).The system uniformly generates a specification for each model to form a learnware and accommodates it.As the key component, a specification should represent the capabilities of the model while preserving developer's original data.Based on the specifications, the learnware dock system can identify and assemble existing learnwares for users to solve new machine learning tasks.Recently, based on reduced kernel mean embedding (RKME) specification, a series of studies have shown the effectiveness of the learnware paradigm theoretically and empirically.However, the realization of a learnware dock system is still missing and remains a big challenge.This paper proposes Beimingwu, the first open-source learnware dock system, providing foundational support for future research.The system provides implementations and extensibility for the entire process of learnware paradigm, including the submitting, usability testing, organization, identification, deployment, and reuse of learnwares.Utilizing Beimingwu, the model development for new user tasks can be significantly streamlined, thanks to integrated architecture and engine design, specifying unified learnware structure and scalable APIs, and the integration of various algorithms for learnware identification and reuse.Notably, this is possible even for users with limited data and minimal expertise in machine learning, without compromising the raw data's security.The system facilitates the future research implementations in learnware-related algorithms and systems, and lays the ground for hosting a vast array of learnwares and establishing a learnware ecosystem.The system is fully open-source and we expect the research community